Climate‐driven shifts in leaf senescence are greater for boreal species than temperate species in the Acadian Forest region in contrast to leaf emergence shifts

Abstract The Acadian Forest Region is a temperate‐boreal transitional zone in eastern North America which provides a unique opportunity for understanding the potential effects of climate change on both forest types. Leaf phenology, the timing of leaf life cycle changes, is an important indicator of the biological effects of climate change, which can be observed with stationary timelapse cameras known as phenocams. Using four growing seasons of observations for the species Acer rubrum (red maple), Betula papyrifera (paper/white birch) and Abies balsamea (balsam fir) from the Acadian Phenocam Network as well as multiple growing season observations from the North American PhenoCam Network we parameterized eight leaf emergence and six leaf senescence models for each species which span a range in process and driver representation. With climate models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) we simulated future leaf emergence, senescence and season length (senescence minus emergence) for these species at sites within the Acadian Phenocam Network. Model performances were similar across models and leaf emergence model RMSE ranged from about 1 to 2 weeks across species and models, while leaf senescence model RMSE ranged from about 2 to 4 weeks. The simulations suggest that by the late 21st century, leaf senescence may become continuously delayed for boreal species like Betula papyrifera and Abies balsamea, though remain relatively stable for temperate species like Acer rubrum. In contrast, the projected advancement in leaf emergence was similar across boreal and temperate species. This has important implications for carbon uptake, nutrient resorption, ecology and ecotourism for the Acadian Forest Region. More work is needed to improve predictions of leaf phenology for the Acadian Forest Region, especially with respect to senescence. Phenocams have the potential to rapidly advance process‐based model development and predictions of leaf phenology in the context of climate change.


| INTRODUC TI ON
Phenology, the timing of recurrent biological events, is influenced by climate and therefore an important indicator of the biological effects of climate change. To optimize growing season length and reproduction potential while avoiding exposure of vulnerable tissues to adverse conditions, plants undergo annual changes that are timed relative to environmental cues such as temperature and daylength (Vitasse et al., 2013). In the late growing season following budset, hormones from distal buds and leaves suppress bud development in what is known as paradormancy (Cline & Deppong, 1999). Following this phase, plants enter a state known as endodormancy or dormancy from autumn to winter, in which internal mechanisms within the bud limit bud cell growth. After sufficient exposure to chilling temperatures, plants enter ecodormancy or quiescence in which suboptimal growing conditions limit cell growth. Following sufficient exposure to warm temperatures, known as 'forcing', and sufficient daylength, ecodormancy release is observed as bud burst in which new leaves become visible (Delpierre et al., 2016). Later in the growing season, plants undergo leaf senescence and dormancy induction as daylength is shortened and temperatures become cooler (Beil et al., 2021;Caffarra et al., 2011).
Climate change is altering the timing of plant phenological events through changes in seasonal temperature and moisture regimes (Cleland et al., 2007;Kunkel et al., 2004;Piao et al., 2019;Scheifinger et al., 2003). Recent warming has generally led to earlier leaf emergence and delayed leaf senescence for most mid to highlatitude tree species, culminating in an extension of the growing season (Estiarte & Peñuelas, 2015;Peñuelas & Filella, 2009;Polgar & Primack, 2011). Changes in leaf phenology have important implications for a range of processes on various spatiotemporal scales, including carbon cycling, water cycling, ecological interactions, susceptibility to unfavourable growing conditions or events and long-term biogeographical range shifts (Chuine & Régnière, 2017;Cleland et al., 2007;Kharouba et al., 2018;Meier et al., 2021;Morin et al., 2009;Pureswaran et al., 2019;Renner & Zohner, 2018;Spafford et al., 2023). Consequently, characterizing and predicting future changes in leaf phenology is important for environmental and natural resource planning and climate change adaptation. Predicting future patterns in leaf phenology with increasing surface temperatures is challenging however due to a limited understandings of drivers and evolved cues, especially for leaf senescence Delpierre et al., 2016;Gallinat et al., 2015;Keenan & Richardson, 2015;Piao et al., 2019).
Process-based modelling of leaf phenology can provide insight into the species-specific responses of leaf phenology to climate change and aid in predicting future leaf phenology patterns. Previous efforts have provided insight into potential future responses to climate change. Examples include shortened leaf colouration periods in autumn due to warming, heat stress or moisture stress (Xie, Wang, et al., 2018;Zohner & Renner, 2019), as well as non-linear leaf emergence responses to further warming due to the constraining influence of photoperiod and chilling controls Moon et al., 2021). Studies have also found evidence for additional nuanced controls of leaf phenology, such as bud albedo, interdependence between spring and autumn phenology, carbon uptake capacity limitation, response to biomass loss, variable sensitivity to drivers and others (Keenan & Richardson, 2015;Lang et al., 2019;Piao et al., 2019;Vitasse et al., 2021). Local-scale experimental studies have developed valuable insights for process-based modelling, though Wolkovich et al. (2012) reported that experimental studies may considerably underestimate phenological responses to warming relative to long-term observations. Relatively few stud- ies have yet explored species-specific process-based modelling employing observations over large regions to examine how broad controls in leaf phenology differ among species in natural contexts , as well as potential responses to future climate warming.
While databases of leaf phenology observations are now globally extensive, there has been sparse in-situ coverage of the Acadian Forest Region, especially for the Canadian province of Nova Scotia.
The Acadian Forest Region is a temperate-boreal transitional forest zone in eastern Canada and northeastern United States (Rowe, 1972;Taylor et al., 2020; Figure 1). Therein, species that typically grow in a temperate climate zone can be found alongside species that typically grow in a boreal climate zone. The common Acadian species Acer rubrum (commonly known as red maple), Betula papyrifera (white/paper birch) and Abies balsamea (balsam fir) have contrasting geographic distributions in North America. Acer rubrum is a more temperate-climate-suited species that can be found growing as far south as Florida. Betula papyrifera and Abies balsamea are more boreal-climate-suited species that are relatively uncommon south of the midwestern US (McKenney et al., 2007(McKenney et al., , 2014. The Acadian Forest Region presents an opportunity for monitoring the in-situ effects of climate change through leaf phenology for both temperate and boreal-typical species. Boreal species within the Acadian Forest Region such as Betula papyrifera and Abies balsamea are near the southern limits of their biogeographical range, while temperate species within the Acadian Forest Region such as Acer rubrum are near the northern limits of their range (Fisichelli et al., 2014;Pearson & D'Orangeville, 2022). Phenology is a trait that constrains where species can survive, as poorly timed phenology can lead to damage from changing environmental conditions, leading to often greater freeze injury risk for non-native species Zanne et al., 2018). Trees which are located near their range limits may be more susceptible to environmental

T A X O N O M Y C L A S S I F I C A T I O N
Biogeography, Botany, Ecosystem ecology, Global change ecology, Global ecology change (Körner et al., 2016;Wang et al., 2021). The Acadian Forest Region is, therefore, especially vulnerable to future changes in temperature and moisture regimes and models have predicted a compositional decline of boreal species due to warming temperatures outside of the optimal biogeographical climate envelopes for these species (Taylor et al., 2017).
In addition, the Acadian Forest Region is subject to extreme weather in the form of hurricanes that lead to windthrow of shallow-rooted coniferous species such as Abies balsamea (Taylor et al., 2019(Taylor et al., , 2020. If the timing of fall leaf senescence is further delayed in the future, this could also make broadleaf species more susceptible to wind damage due to the added surface area (Gong et al., 2021). In the spring, increased climate variability leads to an increased risk of leaf-damaging frost events, which is compounded by the already highly dynamic nature of weather patterns in the maritime region of Canada (Augspurger, 2013; Garbary & Hill, 2021;Steenberg et al., 2013). Trees within the Acadian Forest Region may also be at risk of deleterious drought effects as climate models predict an increased frequency and intensity of droughts, and phenology may play an important role in determining drought resilience (Pearson & D'Orangeville, 2022;Sánchez-Pinillos et al., 2022). The Acadian Forest Region therefore presents a unique and complex forest ecosystem, and better understanding of the leaf phenology of species therein and the F I G U R E 1 The distribution of the Boreal, Acadian and Temperate Forest Regions within North America (left) and the distribution of Acer rubrum, Betula papyrifera and Abies balsamea within North America (right). The 'Acadian Forest Region' depiction is based on forest composition and stand characteristics and is distinct from more detailed ecozone and ecosite classifications which incorporate a greater variety of environmental variables (Neily et al., 2013). The precise extent of the Acadian Forest Region differs among sources (Rowe, 1972;Two Countries One Forest, 2014). potential effects of climate change are needed to predict future ecology and carbon uptake.
Understandings and predictions of future leaf phenology patterns in the eastern Acadian Forest Region are limited due to a lack of observational data, compounded by a highly variable climatic regime (Garbary & Hill, 2021;MacLean et al., 2022;Pearson & D'Orangeville, 2022;Steenberg et al., 2013;Taylor et al., 2020). A study comparing climate normals across Nova Scotia from 1961 to 1990 and 1991 to 2020 found that warming in the autumn has been more pronounced relative to spring, with a larger relative increase in the number of frost-free days in autumn (Garbary & Hill, 2021). Therefore, leaf senescence observations and modelling are crucial in addition to spring leaf emergence to understand the entire growing season phenology implications of climate change for the Acadian Forest Region. Inter-continental scale studies have found differing controls of phenology in North America versus Europe and Asia due to historical weather patterns (Zohner et al., 2020).
Even within North America, phenological responses to environmental drivers and cues vary regionally (Melaas et al., 2016). This suggests that regional species-specific observations are needed to develop confident predictions of the response of vegetation to climate change throughout the 21st century for the Acadian Forest Region.
To better understand the environmental controls of leaf phenology for Acadian Forest Region tree species, we used phenocams to monitor the leaf phenology of three tree species across a natural climate gradient in the Canadian province of Nova Scotia throughout the 2019-2022 growing seasons. We also accessed records of leaf phenology across North America using the PhenoCam Network database. We selected the temperate-climate-suited species Acer rubrum as well as the more boreal-climate-suited species Betula papyrifera and Abies balsamea. These species are common to the Acadian Forest Region and currently monitored throughout the Acadian Phenocam Network and the PhenoCam Network. In this study, we aim to parameterize a variety of species-specific processbased models of leaf phenology and simulate leaf phenology and growing season length for Acer rubrum, Betula papyrifera and Abies balsamea under future climate change scenarios.

| Acadian Phenocam Network
To monitor the leaf phenology of Acadian tree species we installed phenocams at 12 sites in the Canadian province of Nova Scotia before the onset of the 2019 growing season (Figure 2). These selected sites were upland, zonal forest sites with sufficient soil nutrients and moisture profiles to support long-lived, late-successional species and forests where successional pathways are dictated by climate and not constrained by site conditions (Baldwin et al., 2019). The ecosystem types selected-called ecosites in Nova Scotia's Forest Ecosystem Classification system (Neily et al., 2013)-had both intermediate soil moisture regimes (i.e. fresh) and soil nutrient levels.
Mixedwood stands are common on these sites and include broadleafed species like Acer rubrum (red maple), Betula alleghaniensis (yellow birch) and B. papyrifera (white/paper birch) and conifer species like Abies balsamea (balsam fir) and Picea rubens (red spruce). These phenocams were operational throughout the 2019-2020 growing seasons, with several observation gaps in the autumn of 2019 and spring of 2020 due to camera malfunctioning. In the 2020 growing season, we replaced these cameras with cellular trail cameras.
Overall, we observed leaf emergence over the 2019-2022 growing seasons and leaf senescence over the 2019-2021 growing seasons.
The elevation for our sites ranges from 88 to 322 m above sea level, with most sites located below 200 m.  (Filippa et al., 2016;R Core Team, 2022). The time series of images for each site were reviewed to ensure ROIs were delineated without interference from background elements. The species identification of each ROI was confirmed manually in the field.

F I G U R E 2
We classified trees with heights below 5 m as immature and excluded these from analyses, as these tend to exhibit an earlier leaf emergence than mature or canopy-height conspecific trees, occluding climatic influences (Vitasse & Basler, 2014).
Slight shifts in the field of view of each camera due to station maintenance over time were accommodated by creating separate analysis ROI coordinates for images before and after each shift using the 'locator()' function in the graphics package in R (R Core Team, 2022). Where a tilt in the field of view was detected, new ROIs were carefully delineated to match the targets of the ROIs from the previous field of view. The 'extractVIs()' function in the phenopix package was used to extract average red, green and blue colour channel intensity values within each ROI for each image. To extract the greenness time series, we calculated the green chromatic coordinate (G CC ) or relative greenness as is shown in Equation 1.
where B G corresponds to the intensity (brightness) of the green colour channel, B R to the intensity of the red colour channel, and B B to the intensity of the blue colour channel. The G CC represents the intensity of the green colour channel versus the total intensity of all colour channels. We then filtered time series by three-day moving window 50th percentiles of G CC values to remove both high and low outliers (Peltoniemi et al., 2018;Richardson, Hufkens, Milliman, Aubrecht, Chen, et al., 2018;. For further noise reduction, we applied an adapted version of the PhenoCam Network protocols. We exchanged outliers detected as four times greater than the standard deviations of residuals for the upper threshold and two times greater than the standard deviations of residuals for the lower threshold with locally estimated scatterplot smoothing (LOESS) curve-fitted values to further prioritize the removal of anomalous G CC declines (Richardson, Hufkens, Milliman, Aubrecht, Chen, et al., 2018;. For smoothing over the dormant period, we exchanged original dormant period G CC values with that of the dormant season mode, calculated as the lowest local maxima in a density plot of G CC values for a given year. We calculated the timing of leaf emergence and senescence as 50% of the amplitude of rising and falling G CC curves ( Figure 3). Growing season length was calculated as the time between leaf emergence and senescence. To obtain phenology estimates at the site level, we selected sites with at least three individuals of a given species present and averaged each individual phenocamderived phenology date to produce a site-level observation for each species and site-year, which are shown in Section A2: Tables A5 and A6 in Appendix A for leaf emergence and senescence, respectively.
The Moultrie M-50 and Spypoint Link-Evo trail camera models deployed in this study do not have the option to fix the image white balance. Spurious vegetation transition signals may arise due to an automatic white balance (Richardson, Hufkens, Milliman, Aubrecht, Chen, et al., 2018;. To ensure that the colour channel patterns observed through the cameras across our sites were due to changes in leaf canopy development rather than colour scaling artefacts, we utilized several means of validation and of quality control: (1) installation of grey non-reflective reference panels in the field of view of all cameras in 2020 onwards and normalization of veg- site-years (Ahrends et al., 2009;Klosterman et al., 2014;Kosmala et al., 2016;Peltoniemi et al., 2018), and (5) comparison of leaf phenology derived from a Spypoint camera to that of a fixed white balance Brinno camera (https://brinno.com/pages/ produ ct-tlc20 0pro) at an external site. For both manual field and visual observations, we considered leaf emergence to occur when most leaves had emerged entirely from bud scales such that leaf midribs were visible and leaf senescence to occur when most leaves had begun to show autumn colouration. Curve-estimated leaf emergence dates for the three

| PhenoCam Network
To ensure our phenology model training dataset was representative

| Leaf emergence models
We explored a variety of leaf emergence models with varying de-  Hufkens et al., 2018;Wang, 1960). The Thermal Time with Sigmoidal Temperature Response model (TTs) also accumulates forcing above a base temperature until a critical threshold is reached and leaf emergence occurs, though with a sigmoidal accumulation function (Basler, 2016;Hänninen, 1990;Hufkens et al., 2018;Kramer, 1994). The Photo-Thermal Time model (PTT) accumulates forcing above a base temperature in a linear fashion adjusted by daylength (Basler, 2016;Črepinšek et al., 2006;Hufkens et al., 2018;Masle, 1989). The Photo-Thermal Time with Sigmoidal Temperature Response model (PTTs) also accumulates forcing above a base temperature with a sigmoidal function adjusted by daylength until a critical threshold is reached and leaf emergence occurs (Basler, 2016;Črepinšek et al., 2006;Hänninen, 1990;Hufkens et al., 2018;Kramer, 1994;Masle, 1989 (Basler, 2016;Cannell & Smith, 1983;Hufkens et al., 2018;Murray et al., 1989). The Sequential Model (SQ) assumes that chilling requirements are fulfilled prior to the onset of forcing accumulation with a bell-shaped chilling temperate response function. Once a critical threshold in chilling accumulation is reached, forcing accumulates until another critical threshold is reached and leaf emergence occurs (Basler, 2016;Hänninen, 1990;Hufkens et al., 2018;Kramer, 1994). Finally, the Dormphot model ( All leaf emergence models were applied using the phenor package in R with species-specific training and validation for each available site-year . Model parameterization for each species was optimized using the initial parameter ranges from Hufkens et al. (2018) and general simulated annealing with the op-timize_parameters function within the phenor package which is an extension of the GenSA optimization function within the GenSA package in R (Xiang et al., 2013). General simulated annealing is a technique of optimization that is analogous to the process of metal cooling (Chuine et al., 2000). The GenSA function is based on the Boltzmann machine and Cauchy machine simulated annealing approaches Tsallis & Stariolo, 1996). General simulated annealing was constrained with a maximum of 50,000 iterations and a starting temperature of 10,000 for each annealing to achieve a global minimum in root mean squared error (RMSE).
Equations and optimal parameters for each model from this process are shown in Section A1: Tables A1 and A2 in Appendix A, respectively.

| Leaf senescence models
We Response model is used to provide an estimated leaf emergence timing that incorporates both forcing and daylength (Keenan & Richardson, 2015;Liu et al., 2020). The timing of the preceding leaf emergence relative to the long-term average estimated from a 30-year daily average temperature window influences the critical threshold for leaf senescence for these two models.
The parameter ranges for each leaf senescence model were determined by reviewing local climate data, the available literature for each model and the range of optimal values from Liu et al. (2020).
All leaf senescence models were applied in R with species-specific training and validation for each available site-year. As with the leaf emergence models, senescence model parameterizations were optimized for each species using general simulated annealing with a maximum of 50,000 iterations and a starting temperature of 10,000 for each annealing to find model parameters that corresponded to a global minimum RMSE. Equations and optimal parameters for each model from this process are shown in Section A1: Tables A3 and A4, respectively in Appendix A. To examine the regional transferability of each model, we performed a smaller scale calibration and validation with observations just from the Acadian Phenocam Network. To examine the regional specificity of the models, we also trained the models with all observations and then validated the models with just observations from the Acadian Phenocam Network. We also calculated the bias between model-estimated and observed phenophases for each validation exercise to examine potential systematic over-and under-estimations. To examine globally parameterized model performances for warm siteyears and for cold-site years, we selected validation site-years with annual average temperatures within the top 25th percentile of annual average temperatures for warm site-years and within the lower 25th percentile for cold site-years. We then computed the RMSE and bias for these site-years for globally parameterized models. To examine how well these models performed with independent validation, we also performed a leave-one-out cross-validation and a k-fold crossvalidation. For efficiency with these two validations, each model was parameterized with a maximum of 4000 iterations. The value of 'k' was allowed to vary such that each sample group had five or more samples, which is an effective model quality assessment approach with a range in dataset sizes (Jiang & Wang, 2017;Yadav & Shukla, 2016).

| Obtaining driver data
To train each phenology model, we obtained daily weather data for each site-year in both the Acadian and PhenoCam Network datasets up until 2021 from the Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1 dataset using the daymetr package in R Thornton et al., 2022).
Long-term mean temperatures from 1980 to 2021 were also obtained for each site from this dataset using the daymetr package.
We used 1 km × 1 km grids with the nearest central coordinates to each site location for each site-year.
To project leaf phenology for each of our three species for the  (Cannon et al., 2015). We extracted data from the 300-arc second spatial resolution (1/12°, ~10 km) grid cells with central coordinates closest to our site locations.

| Leaf phenology training data
Leaf phenology patterns in relation to temperature were similar for the Acadian and PhenoCam Networks (Figure 4). Within the Acadian Phenocam Network, each species generally showed a spatial pattern in leaf emergence dates reflecting the climate gradient used in their establishment, with later emergence at the colder northeastern sites and earlier at the warmer southwestern sites. In contrast, leaf senescence dates for each species did not exhibit a distinct climate pattern. Surprisingly, several sites in the warmer region of the Acadian Network had earlier leaf senescence dates than that of the colder re-

| Leaf emergence models
No one leaf emergence model had exceptional performance relative to the other models across species and validation exercises, though all generally managed to outperform the Null model ( Figures 5-7).
For the global validation, the Dormphot and M1 models were among the top two models with the lowest RMSE relative to other models for each species. All eight models outperformed the Null model for F I G U R E 4 Leaf phenology patterns according to seasonal or annual temperature averages for both the Acadian and PhenoCam Networks.

F I G U R E 5
Root mean squared error and mean bias for each of eight leaf emergence models and a Null model for seven different validation exercises for Acer rubrum. The total number of Acer rubrum leaf emergence observations for is shown on the top right. The model with the lowest root mean squared error for each validation exercise is denoted with an asterisk. each species and validation, each process model having typically a week or less in RMSE for Acer rubrum ( Figure 5) and Betula papyrifera ( Figure 6) and less than 2 weeks for Abies balsamea (Figure 7).

| Leaf senescence models
Similarly, no one leaf senescence model had exceptional global performance relative to the others, with some failing to outperform the Null model for several validations (Figures 8-10). For the global validation, the Delpierre model was optimal for Abies balsamea ( Figure 10) and Betula papyrifera (Figure 9), while the Dormphot model with just Dormancy Induction was optimal for Acer rubrum (Figure 8). and cold site-years, respectively, for Acer rubrum (Figure 8), though mixed and early leaf senescence for Abies balsamea ( Figure 10) and Betula papyrifera (Figure 9). Despite its occasionally preferable RMSE scores, the simple trigger-based White model was often among the top three models with the greatest absolute bias across validation exercises for each species.

| Projected climate
Under the RCP 8.5 scenario, annual average temperatures in the year 2100 were projected to increase by about 4°C from 7 to 11°C relative to 1990-2020 across our 12 sites (Figure 11). For the RCP 2.6 scenario, a more moderate temperature increase of about 1°C was projected. The projected temperature change for northeastern sites was equivalent to warming them to the temperature of the southwestern sites for each emissions scenario. The projected annual average temperatures for our sites in 2100 under the RCP 8.5 scenario are within the range of training dataset annual average temperatures for Acer rubrum (annual average ~4-18°C) and Abies balsamea (annual average ~0-11°C), though slightly beyond the range for Betula papyrifera (annual average ~1-9°C; Figure 4).

| Projected leaf phenology
For each site, phenology model, species, the projected phenology and season length varied each year throughout the 21st century among F I G U R E 9 Root mean squared error and mean bias for each of six leaf senescence models and a Null model for seven different validation exercises for Betula papyrifera. The total number of Betula papyrifera leaf senescence observations for is shown on the top right. The model with the lowest root mean squared error for each validation exercise is denoted with an asterisk.
F I G U R E 1 0 Root mean squared error and mean bias for each of six leaf senescence models and a Null model for seven different validation exercises for Abies balsamea. The total number of Abies balsamea leaf senescence observations for is shown on the top right. The model with the lowest root mean squared error for each validation exercise is denoted with an asterisk.
climate models (Figure 12). For each phenology model, variation in predicted leaf emergence dates among climate models was generally greater each year than variation in predicted leaf senescence dates. The predicted leaf emergence for some years was anomalous with respect to adjacent years and the longer record for several climate models.
The projected change in the timing of leaf emergence, leaf senescence and the corresponding season length for the mid-and late-21st century for each phenology model and emissions scenario is shown in Figure 13 for Acer rubrum, Figure 14 for Betula papyrifera and Figure 15 for Abies balsamea. For each species and phenology model, leaf emergence is projected to advance and leaf senescence is projected to be delayed, though by varying degrees among species and models. Acer rubrum due to a more pronounced leaf senescence delay. The species-specific projections from our models agree with the findings of a 5-year experimental study by Montgomery et al. (2020) in which species with a higher latitude of origin had a greater response to experimental warming. Given the expected northward expansion of the more temperate-climate-suited species like Acer rubrum, the species-specific differences in projected phenology and

| Model performances
Our study provided a novel demonstration of the parameterization of species-specific leaf phenology models using phenocam observa- For leaf senescence modelling, incorporating the influence of the preceding spring leaf emergence did not lead to overall improved model performance, though it improved regional applicability for the Acadian Phenocam Network in the case of Acer rubrum. Model performance among leaf senescence models was consistent between models relative to leaf emergence. Prediction error among leaf senescence models was approximately twice that of leaf emergence models. The parameterization of several of the leaf senescence models used in this study is mathematically similar to leaf emergence models, despite the distinction in the relationship for each phenophase to growing season temperatures (Figure 4). This indicates there is ample potential for improving leaf senescence models. Future leaf senescence model development would benefit from the exploration of novel cues and parameterizations that are more distinct from leaf emergence models.
The environmental context for both training and validation datasets was highly influential on ultimate model performance. We F I G U R E 1 5 Predicted change in Abies balsamea leaf phenology and growing season length for each leaf phenology model and RCP scenario. Uncertainty bars denote the 5th-95th percentile change values.
found variable model performance when models were validated with warm site-years versus cold site-years, with different optimal models depending upon each context for both leaf emergence and senescence. Another study using satellite-based observations and modelling also found model performances varied based on validation temperatures (Fu et al., 2014). Together this performance bias suggests that despite the satisfactory performance of these phenology models, novel parameterizations and potentially additional drivers are needed to improve the models' general applicability to a range of seasonal conditions.  (Fang et al., 2022;Gallinat et al., 2015).
For the global combination of samples in our study, complex leaf emergence models representing the combination of dormancy induction, endodormancy and ecodormancy release slightly outperformed more simple models for several validation exercises.
In contrast, when we conducted leave-one-out and k-fold crossvalidations, we found that complex models performed less well than simple models such as the M1 model. For the pooled combination of samples within our study, complex leaf senescence models incorporating the influence of the preceding ecodormancy release generally performed less well than simpler models. This may be due to the distinction in season length across regions, such that the parameterization for the constraint of growing season length in one region is less applicable to another region. When we conducted leave-oneout and k-fold cross-validations for leaf senescence, we again found that complex models performed less well than simple models such as the trigger-based White model. Together these findings are consistent with Basler (2016) for both global and regional transferability error evaluations in relation to model complexity. This is likely due to the trade-off between global performance and local specificity with complex models, which achieve greater performance with higher validation sample sizes. Globally trained models tended to predict an earlier date of leaf emergence and a later date of senescence than is observed for the Acadian Forest Region. This suggests that both model complexity and training dataset spatial coverage should be considered when parameterizing, validating and developing models.
Additionally, this indicates that relationships between leaf phenology and environmental influences may vary in a non-linear fashion between regions, calling for more local and regional scale studies to inform broad leaf phenology mechanism understandings.
Despite the broad range of observations and training contexts in our study, all species and validations agree in that simple Thermal

| Future leaf phenology for Acadian Forest region
In the Acadian Forest Region, future leaf emergence in the context of both moderate and high emissions will be earlier while leaf senescence will be later, though more work is needed to better predict the magnitude of these changes. The ensemble of models in our study predicts about 2, 2-3 and 3 weeks advance in leaf emergence and 1, 2-3 and Caution is warranted in the interpretation of this apparent diminishing response of leaf emergence phenology to temperature change as more work is needed to thoroughly examine this finding. The aggregated weather patterns from the CMIP5 models may underestimate local interannual variability, omit the important role of anomalous seasonal conditions and underestimate phenological temperature sensitivity due to uncertainty in projected temperatures (Keenan et al., 2020 (Xie, Wang, et al., 2018). The projected delay in leaf colouration onset therein was about 1 week for Acer rubrum and just over 2 weeks for Betula papyrifera (Xie, Wang, et al., 2018). While a limited advancement in leaf emergence overtime translates to a potentially limited lengthening in the growing season, the expected continuous delay in leaf senescence may still promote increased seasonal carbon uptake as Wu et al. (2013) found that the timing of leaf senescence was more influential on seasonal carbon uptake than leaf emergence. If alternatively, the timing of leaf senescence depends upon the timing of leaf emergence as some studies have found and our Acadian Network observations suggest (Keenan & Richardson, 2015;Liu et al., 2020), this may constrain the length of growing season and subsequently carbon uptake. Another important consideration is the occurrence of anomalously early leaf emergence timings with respect to adjacent years or the long-term record, as this makes leaves susceptible to frost damage and may lead to carbon losses (Augspurger, 2009(Augspurger, , 2013Chamberlain et al., 2019;Gu et al., 2008;Montgomery et al., 2020;Richardson, Hufkens, Milliman, Aubrecht, Furze, et al., 2018;Vitasse et al., 2018). In addition, while not represented in our models, climate change has the potential to influence leaf phenology through alternative effects including changes in moisture availability and disturbance legacy effects (Angulo-Sandoval et al., 2004;Meier et al., 2021;Spafford et al., 2023;Wu et al., 2022).
Together this indicates more regional-scale species-specific observation and modelling efforts are needed to understand regionally variable controls of leaf senescence as well as leaf emergence.

| Future implications
A substantial annual temperature increase of ~4°C is predicted for the Acadian Forest Region under a high emissions scenario. This change in conditions will surpass the optimal growing temperature for boreal-climate-suited species like Abies balsamea and Betula papyrifera (Dhar et al., 2014;Frank, 1990;Wang et al., 1998).
Previous studies have predicted a decline in the proportion of boreal species in the Acadian Forest by the late 21st century due to suboptimal growing conditions (Taylor et al., 2017), which is supported by the dramatic shifts predicted with our phenology models for the 21st century. An experimental study by Vaughn et al. (2021) found reduced mortality and sustained height growth in the context of drought for temperate-climate-suited Acer rubrum in comparison to colder-climate-adapted species such as Abies balsamea. In a synthesis of the effects of climate change on Abies balsamea regeneration, Collier et al. (2022) alternatively found that the adverse impacts on Abies balsamea may occur with a complex combination of processes including reduced competitive fitness and mortality of overstory trees. The potentially extensive delay in the timing of leaf senescence found in our study for boreal species in the Acadian Forest Region suggests that future nutrient resorption success may be diminished for these species in comparison to temperate species. Leaf senescence functions primarily as a means of conserving nutrients for deciduous tree species which are used in the development of new leaves in the following spring (Estiarte & Peñuelas, 2015). A delayed senescence leads to a greater risk of nutrient losses due to fall hurricanes prematurely removing or damaging leaf tissues, disrupting the normal course of nutrient recycling achieved through senescence. Together this indicates that more boreal-typical species like Abies balsamea in the Acadian Forest may suffer declined growth, greater mortality, reduced fitness, a shift in optimal biogeographical envelopes beyond their current range and perhaps a substantially reduced longevity in the context of climate change in the 21st century.
This has important implications for forest structure and ecological interactions across the Acadian Forest Region, which is already vulnerable due to most species therein being near the limit of their ranges (Fisichelli et al., 2014;Körner et al., 2016;Pearson & D'Orangeville, 2022;Wang et al., 2021).
The predicted lengthening of the carbon uptake period prompted by an earlier leaf emergence and later leaf senescence for each species found in our study may be expected to lead to increased carbon uptake (Wu et al., 2013). On the contrary, increased mortality, disturbance and suboptimal growing conditions in the context of climate change may lead to reduced carbon uptake across the Acadian Forest Region (Taylor et al., 2020), losses which may more than compensate for potential carbon uptake gains from warming (D'Orangeville et al., 2018). In addition, several studies have found that a longer leafing period does not always lead to increased carbon uptake in the form of stable woody biomass (Camarero et al., 2022;Čufar et al., 2015;Delpierre et al., 2017;Dow et al., 2022;Fang et al., 2020;Marchand et al., 2021). Further, the potential for the increased establishment of more temperateclimate-suited species like Acer rubrum may be limited throughout the 21st century due to the physical occupation of space by boreal species (Taylor et al., 2017). As species ranges shift northward and weather patterns exhibit more frequent and intense anomalies in the context of climate change, there is also a greater potential for carbon losses due to late spring frost events which may be more damaging for species outside their native ranges Zanne et al., 2018). Therefore, the composition of the Acadian Forest Region is likely to change under a high emissions scenario by the late 21st century, as well as the capacity for carbon uptake within the Acadian Forest Region. Understanding which phenological strategy is optimal in response to such changes is necessary for promoting the migration of suitable species and provenances therein (Ding & Brouard, 2022).
Beyond biogeochemistry, the constrained delay in leaf senescence for temperate species such as Acer rubrum found in our study has important implications for the autumn colour ecotourism industry in the Acadian Forest Region (Ivakhiv, 2005;Spencer & Holecek, 2007). Acer rubrum are responsible for the vibrant red colours which contrast with the predominant yellow autumn colouration of other species in much of the Acadian Forest Region. Divergence in the relative timing of leaf senescence for deciduous species of the Acadian Forest may have important implications for the future appearance and appeal of the fall colours, as well as for ecological interactions between species (Cleland et al., 2007;Kharouba et al., 2018;Renner & Zohner, 2018). Cleland et al. (2012) found that species that do not respond as acutely to temperature changes may in fact be at a disadvantage in terms of ecological performance relative to other species, despite the better potential avoidance of suboptimal growing conditions with such strategies.

| CON CLUS ION
The

DATA AVA I L A B I L I T Y S TAT E M E N T
Data used in this study are available in Section A2 in Appendix A and the PhenoCam V2.0 dataset (Seyednasrollah, Young, Hufkens, Milliman, Friedl, Frolking, Richardson, Abraha, et al., Basler, D. (2016). Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across Central Europe. Agricultural and Forest Meteorology, 217, 10-21. Beil, I., Kreyling, J., Meyer, C., Lemcke, N., & Malyshev, A. V. (2021)

TA B L E A 1
Leaf emergence process models included in this study for estimating the timing of the start of season (SOS) or leaf emergence. Note that each leaf emergence site-year runs from September 1st to August 31st such that a starting date of January 1st corresponds to a t 0 of 103. R frc and R chl denote the rate of forcing and chilling, respectively. S frc and S chl denote the accumulated state of forcing and chilling, respectively. DR t and DR p denote the rate of dormancy induction based on temperature and photoperiod, respectively. S DR denotes the state of dormancy induction accumulation.

Variables, parameters, and release process(es) included Equation Reference
Sequential (SQ) -Chilling starting date t 0c -Forcing starting date t 0f -Daily mean temperature T i -Minimum temperature for chilling accumulation T min -Optimal temperature for chilling accumulation T opt -Maximum temperature for chilling accumulation T max -Threshold for forcing accumulation C req -Base temperature for forcing accumulation T b -Critical threshold for leaf emergence F crit Endodormancy & Ecodormancy Hufkens et al. (2018), Basler (2016), Kramer (1994), Hänninen (1990) Dormphot (DP) -Dormancy induction sensitivity parameter a -Daily mean temperature T i -Response parameter for dormancy induction accumulation b -Daylength L i -Threshold daylength for dormancy induction accumulation L crit -Threshold for dormancy induction D crit -Chilling sensitivity parameter c -Rate of chilling accumulation parameter d -Forcing sensitivity parameter h L -Chilling threshold parameter for forcing accumulation C req -Daylength sensitivity parameter gT -Forcing accumulation parameter df -Critical threshold for leaf emergence F crit Dormancy Induction, Endodormancy, & Ecodormancy

TA B L E A 2
Optimal parameters and initial parameter range in square brackets for or each leaf emergence model using general simulated annealing. Note that each leaf emergence site-year runs from September 1st to August 31st such that a starting date of January 1st corresponds to a t 0 of 103.

TA B L E A 2 (Continued)
TA B L E A 3 Leaf senescence process models included in this study. Note that each leaf senescence site-year runs from January 1st to December 31st such that a starting date of January 1st corresponds to a t 0 of 1. R tp , R t , and R p denote the rate of temperature cooling and photoperiod reducing, the rate of temperature cooling, and the rate of photoperiod reducing, respectively. S tp , S t , and S p denote the accumulated state of temperature cooling and photoperiod reducing, the accumulated state of temperature cooling, and the accumulated state of photoperiod reducing, respectively.

Model Variables, parameters, and process(es) included Equation Reference
Null -Mean date from all observations EOS o None EOS = EOS o -White (WM) -Starting date fixed to July 1st -Daily mean temperature T i -Temperature for combined temperature daylength senescence trigger T b1 -Daylength L i -Daylength for combined temperature daylength senescence trigger L crit -Temperature for singular temperature senescence trigger T b2 -Criteria for senescence: first day of either R tp or R t not equal to 0 Dormancy Induction Liu et al. (2020), White et al. (1997) Delpierre (DM) -Starting date fixed to July 1st -Daily mean temperature T i -Daylength L i -Temperature threshold and parameter for accumulation T b -Daylength threshold and parameter for accumulation L crit -Exponential weight parameter for temperature importance x -Exponential weight parameter for daylength importance y -Critical threshold for leaf senescence F crit Dormancy Induction

Model Variables, parameters, and process(es) included Equation Reference
Delpierre's with Preceding Spring Leaf Emergence (DMs) -Starting date fixed to July 1st -Daily mean temperature T i -Daylength L i -Temperature threshold and parameter for accumulation T b -Daylength threshold and parameter for accumulation L crit -Exponential weight parameter for temperature importance x -Exponential weight parameter for daylength importance y -Threshold modification parameter of anomaly in leaf emergence in the preceding spring relative to the 30-year average estimated with the PTTs model S a -Threshold modification parameter a -Threshold modification parameter b -Critical threshold for leaf senescence F crit

TA B L E A 3 (Continued)
TA B L E A 4 Optimal parameters and initial parameter range in square brackets for or each leaf senescence model using general simulated annealing. Note that each leaf senescence site-year runs from January 1st to December 31st such that a starting date of January 1st corresponds to a t 0 of 1.

Acer rubrum Betula papyrifera Abies balsamea
White (WM) -Temperature for combined temperature daylength senescence trigger T b1 -Daylength for combined temperature daylength senescence trigger L crit -Temperature for singular temperature senescence trigger T b2